How should we measure online learning activity?

The proliferation of Web-based learning objects makes finding and evaluating resources a considerable hurdle for learners to overcome. While established learning analytics methods provide feedback that can aid learner evaluation of learning resources, the adequacy and reliability of these methods is questioned. Because engagement with online learning is different from other Web activity, it is important to establish pedagogically relevant measures that can aid the development of distinct, automated analysis systems. Content analysis is often used to examine online discussion in educational settings, but these instruments are rarely compared with each other which leads to uncertainty regarding their validity and reliability. In this study, participation in Massive Open Online Course (MOOC) comment forums was evaluated using four different analytical approaches: the Digital Artefacts for Learning Engagement (DiAL-e) framework, Bloom's Taxonomy, Structure of Observed Learning Outcomes (SOLO) and Community of Inquiry (CoI). Results from this study indicate that different approaches to measuring cognitive activity are closely correlated and are distinct from typical interaction measures. This suggests that computational approaches to pedagogical analysis may provide useful insights into learning processes.

Comparison Between Automated And Microscopic Analysis In Body Fluids Cytology.

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Agreement for Detecting Glaucoma Progression with the GDx Guided Progression Analysis, Automated Perimetry, and Optic Disc Photography

Purpose: To evaluate the ability of the GDx Variable Corneal Compensation (VCC) Guided Progression Analysis (GPA) software for detecting glaucomatous progression. Design: Observational cohort study. Participants: The study included 453 eyes from 252 individuals followed for an average of 46 +/- 14 months as part of the Diagnostic Innovations in Glaucoma Study. At baseline, 29% of the eyes were classified as glaucomatous, 67% of the eyes were classified as suspects, and 5% of the eyes were classified as healthy. Methods: Images were obtained annually with the GDx VCC and analyzed for progression using the Fast Mode of the GDx GPA software. Progression using conventional methods was determined by the GPA software for standard automated achromatic perimetry (SAP) and by masked assessment of optic disc stereophotographs by expert graders. Main Outcome Measures: Sensitivity, specificity, and likelihood ratios (LRs) for detection of glaucoma progression using the GDx GPA were calculated with SAP and optic disc stereophotographs used as reference standards. Agreement among the different methods was reported using the AC(1) coefficient. Results: Thirty-four of the 431 glaucoma and glaucoma suspect eyes (8%) showed progression by SAP or optic disc stereophotographs. The GDx GPA detected 17 of these eyes for a sensitivity of 50%. Fourteen eyes showed progression only by the GDx GPA with a specificity of 96%. Positive and negative LRs were 12.5 and 0.5, respectively. None of the healthy eyes showed progression by the GDx GPA, with a specificity of 100% in this group. Inter-method agreement (AC1 coefficient and 95% confidence intervals) for non-progressing and progressing eyes was 0.96 (0.94-0.97) and 0.44 (0.28-0.61), respectively. Conclusions: The GDx GPA detected glaucoma progression in a significant number of cases showing progression by conventional methods, with high specificity and high positive LRs. Estimates of the accuracy for detecting progression suggest that the GDx GPA could be used to complement clinical evaluation in the detection of longitudinal change in glaucoma. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. Ophthalmology 2010; 117: 462-470 (C) 2010 by the American Academy of Ophthalmology.

Automated image analysis of lung branching morphogenesis from microscopic images of fetal rat explants

Background: Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour and peripheral airway buds of lung explants during cellular development from microscopic images. Methods: The outer contour was defined using an adaptive and multi-scale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelial was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds were counted as the skeleton branched ends from a skeletonized image of the lung inner epithelial. Results: The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Non-significant differences were found between the automatic and manual results in all culture days. Conclusions: The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lightning characteristics and allowing a reliable comparison between different researchers.

Automated image analysis of lung branching morphogenesis

Regulating mechanisms of branchingmorphogenesis of fetal lung rat explants have been an essential tool formolecular research.This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development frommicroscopic images. Methods.Theouter contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to themanualmethod. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers.

Rapid automated method for on-site determination of sulfadiazine in fish farming: a stainless steel veterinary syringe coated with a selective membrane of PVC serving as a potentiometric detector in a flow-injection-analysis system

Sulfadiazine is an antibiotic of the sulfonamide group and is used as a veterinary drug in fish farming. Monitoring it in the tanks is fundamental to control the applied doses and avoid environmental dissemination. Pursuing this goal, we included a novel potentiometric design in a flow-injection assembly. The electrode body was a stainless steel needle veterinary syringe of 0.8-mm inner diameter. A selective membrane of PVC acted as a sensory surface. Its composition, the length of the electrode, and other flow variables were optimized. The best performance was obtained for sensors of 1.5-cm length and a membrane composition of 33% PVC, 66% onitrophenyloctyl ether, 1% ion exchanger, and a small amount of a cationic additive. It exhibited Nernstian slopes of 61.0 mV decade-1 down to 1.0×10-5 mol L-1, with a limit of detection of 3.1×10-6 mol L-1 in flowing media. All necessary pH/ionic strength adjustments were performed online by merging the sample plug with a buffer carrier of 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid, pH 4.9. The sensor exhibited the advantages of a fast response time (less than 15 s), long operational lifetime (60 days), and good selectivity for chloride, nitrite, acetate, tartrate, citrate, and ascorbate. The flow setup was successfully applied to the analysis of aquaculture waters. The analytical results were validated against those obtained with liquid chromatography–tandem mass spectrometry procedures. The sampling rate was about 84 samples per hour and recoveries ranged from 95.9 to 106.9%.

Automated quantitative analysis of MCC-IMS spectra

Ion Mobility Spectrometry coupled with Multi Capillary Columns (MCC -IMS) is a fast analytical technique working at atmospheric pressure with high sensitivity and selectivity making it suitable for the analysis of complex biological matrices. MCC-IMS analysis generates its information through a 3D spectrum with peaks, corresponding to each of the substances detected, providing quantitative and qualitative information. Sometimes peaks of different substances overlap, making the quantification of substances present in the biological matrices a difficult process. In the present work we use peaks of isoprene and acetone as a model for this problem. These two volatile organic compounds (VOCs) that when detected by MCC-IMS produce two overlapping peaks. In this work it’s proposed an algorithm to identify and quantify these two peaks. This algorithm uses image processing techniques to treat the spectra and to detect the position of the peaks, and then fits the data to a custom model in order to separate the peaks. Once the peaks are separated it calculates the contribution of each peak to the data.

Cerebral perfusion and automated individual analysis using SPECT among an obsessive-compulsive population

OBJECTIVE: To make individual assessments using automated quantification methodology in order to screen for perfusion abnormalities in cerebral SPECT examinations among a sample of subjects with OCD. METHODS: Statistical parametric mapping (SPM) was used to compare 26 brain SPECT images from patients with OCD individually with an image bank of 32 normal subjects, using the statistical threshold of p < 0.05 (corrected for multiple comparisons at the level of individual voxels or clusters). The maps were analyzed, and regions presenting voxels that remained above this threshold were sought. RESULTS: Six patients from a sample of 26 OCD images showed abnormalities at cluster or voxel level, considering the criteria described above, which represented 23.07%. However, seven images from the normal group of 32 were also indicated as cases of perfusional abnormality, representing 21.8% of the sample. CONCLUSION: The automated quantification method was not considered to be a useful tool for clinical practice, for analyses complementary to visual inspection.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Three-dimensional morphological analysis of intracranial aneurysms: a fully automated method for aneurysm sac isolation and quantification

Morphological descriptors are practical and essential biomarkers for diagnosis andtreatment selection for intracranial aneurysm management according to the current guidelinesin use. Nevertheless, relatively little work has been dedicated to improve the three-dimensionalquanti cation of aneurysmal morphology, automate the analysis, and hence reduce the inherentintra- and inter-observer variability of manual analysis. In this paper we propose a methodologyfor the automated isolation and morphological quanti cation of saccular intracranial aneurysmsbased on a 3D representation of the vascular anatomy.

Seizure detection with automated EEG analysis: a validation study focusing on periodic patterns.

OBJECTIVE: To evaluate an automated seizure detection (ASD) algorithm in EEGs with periodic and other challenging patterns. METHODS: Selected EEGs recorded in patients over 1year old were classified into four groups: A. Periodic lateralized epileptiform discharges (PLEDs) with intermixed electrical seizures. B. PLEDs without seizures. C. Electrical seizures and no PLEDs. D. No PLEDs or seizures. Recordings were analyzed by the Persyst P12 software, and compared to the raw EEG, interpreted by two experienced neurophysiologists; Positive percent agreement (PPA) and false-positive rates/hour (FPR) were calculated. RESULTS: We assessed 98 recordings (Group A=21 patients; B=29, C=17, D=31). Total duration was 82.7h (median: 1h); containing 268 seizures. The software detected 204 (=76.1%) seizures; all ictal events were captured in 29/38 (76.3%) patients; in only in 3 (7.7%) no seizures were detected. Median PPA was 100% (range 0-100; interquartile range 50-100), and the median FPR 0/h (range 0-75.8; interquartile range 0-4.5); however, lower performances were seen in the groups containing periodic discharges. CONCLUSION: This analysis provides data regarding the yield of the ASD in a particularly difficult subset of EEG recordings, showing that periodic discharges may bias the results. SIGNIFICANCE: Ongoing refinements in this technique might enhance its utility and lead to a more extensive application.

Non-Destructive Evaluation of Iowa Pavements Phase 2: Development of a Fully Automated Software System for Rapid Analysis/Processing of the Falling Weight Deflectometer Data, 2009

The Office of Special Investigations at Iowa Department of Transportation (DOT) collects FWD data on regular basis to evaluate pavement structural conditions. The primary objective of this study was to develop a fully-automated software system for rapid processing of the FWD data along with a user manual. The software system automatically reads the FWD raw data collected by the JILS-20 type FWD machine that Iowa DOT owns, processes and analyzes the collected data with the rapid prediction algorithms developed during the phase I study. This system smoothly integrates the FWD data analysis algorithms and the computer program being used to collect the pavement deflection data. This system can be used to assess pavement condition, estimate remaining pavement life, and eventually help assess pavement rehabilitation strategies by the Iowa DOT pavement management team. This report describes the developed software in detail and can also be used as a user-manual for conducting simulation studies and detailed analyses. *********************** Large File ***********************

Statistical Analysis of Highway Needs Condition Data: Manual vs. Automated, TR-494

This project examines similarities and differences between the automated condition data collected on and off county paved roads and the manual condition data collected by Iowa Department of Transportation (DOT) staff in 2000 and 2001. Also, the researchers will provide staff support to the advisory committee in exploring other options to the highway need process. The results show that the automated condition data can be used in a converted highway needs process with no major differences between the two methods. Even though the foundation rating difference was significant, the foundation rating weighting factor in HWYNEEDS is minimal and should not have a major impact. In terms of RUTF formula based distribution, the results clearly show the superiority of the condition-based analysis compared to the non-condition based. That correlation can be further enhanced by adding more distress variables to the analysis.

Automated static image analysis as a novel tool in describing the physical properties of dietary fiber

Abstract The growing interest in the usage of dietary fiber in food has caused the need to provide precise tools for describing its physical properties. This research examined two dietary fibers from oats and beets, respectively, in variable particle sizes. The application of automated static image analysis for describing the hydration properties and particle size distribution of dietary fiber was analyzed. Conventional tests for water holding capacity (WHC) were conducted. The particles were measured at two points: dry and after water soaking. The most significant water holding capacity (7.00 g water/g solid) was achieved by the smaller sized oat fiber. Conversely, the water holding capacity was highest (4.20 g water/g solid) in larger sized beet fiber. There was evidence for water absorption increasing with a decrease in particle size in regards to the same fiber source. Very strong correlations were drawn between particle shape parameters, such as fiber length, straightness, width and hydration properties measured conventionally. The regression analysis provided the opportunity to estimate whether the automated static image analysis method could be an efficient tool in describing the hydration properties of dietary fiber. The application of the method was validated using mathematical model which was verified in comparison to conventional WHC measurement results.